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Huu Duy Nguyen
Faculty of Geography, VNU University of Science, Vietnam National University, 334 Nguyen Trai, Thanh Xuan district, Hanoi, Viet Nam

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Journal article
Published: 30 July 2021 in Geoderma Regional
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With population growth, the demand for land resources is expected to increase significantly in the coming decades. Maintaining the integrity of soil distribution requires a remarkable amount of work to deal with agricultural extension. Salinity intrusion monitoring is a crucial process, which directly affects sustainable development, especially in areas affected by global warming and in coastal zones. In recent years, various studies have used the soil-water salinity data to evaluate the spatiotemporal increase in salinity intrusion. This study aims to establish a novel framework for monitoring salinity intrusion using remote sensing and machine learning. It focuses on the salinity intrusion in soil, which affects water availability, food security, human health, etc. Numerous algorithms have been implemented to find the best solution for this issue, including Xgboost (XGR), Gaussian processes, support vector regression, deep neural networks, and the grasshopper optimization algorithm (GOA). A total of 143 samples collected from 2016 to 2020 at 39 measurement stations were divided into two sets: 70% training and 30% testing. Thirty-one independent variables were used to develop the model. Vietnam's Mekong Delta, where the salinity intrusion problem is becoming increasingly serious due to global warming and demographics, was selected as the study area. Each of the proposed models was compared and evaluated by applying various statistical indices such as the root mean square error, coefficient of determination (R2), and mean absolute error. The results show that the prediction model was built successfully by wielding data from the implemented salinity measurement stations, and the XGR-GOA model was better than the other models (R2 = 0.86, RMSE = 0.076, and MAE = 0.065). This finding demonstrates the feasibility of estimating and monitoring salinity intrusion in data-limited regions by integrating optical satellite images and machine learning, which are easily and cost-effectively obtainable. The proposed conceptual methodology in our study is novel and provides additional useful information for the monitoring and management of salinity intrusion not only in Vietnam's Mekong Delta, but also in other sites that have similar natural and anthropological conditions.

ACS Style

Tien Giang Nguyen; Ngoc Anh Tran; Phuong Lan Vu; Quoc-Huy Nguyen; Huu Duy Nguyen; Quang-Thanh Bui. Salinity intrusion prediction using remote sensing and machine learning in data-limited regions: A case study in Vietnam's Mekong Delta. Geoderma Regional 2021, 27, e00424 .

AMA Style

Tien Giang Nguyen, Ngoc Anh Tran, Phuong Lan Vu, Quoc-Huy Nguyen, Huu Duy Nguyen, Quang-Thanh Bui. Salinity intrusion prediction using remote sensing and machine learning in data-limited regions: A case study in Vietnam's Mekong Delta. Geoderma Regional. 2021; 27 ():e00424.

Chicago/Turabian Style

Tien Giang Nguyen; Ngoc Anh Tran; Phuong Lan Vu; Quoc-Huy Nguyen; Huu Duy Nguyen; Quang-Thanh Bui. 2021. "Salinity intrusion prediction using remote sensing and machine learning in data-limited regions: A case study in Vietnam's Mekong Delta." Geoderma Regional 27, no. : e00424.

Journal article
Published: 13 January 2021 in Remote Sensing
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Flood risk is a significant challenge for sustainable spatial planning, particularly concerning climate change and urbanization. Phrasing suitable land planning strategies requires assessing future flood risk and predicting the impact of urban sprawl. This study aims to develop an innovative approach combining land use change and hydraulic models to explore future urban flood risk, aiming to reduce it under different vulnerability and exposure scenarios. SPOT-3 and Sentinel-2 images were processed and classified to create land cover maps for 1995 and 2019, and these were used to predict the 2040 land cover using the Land Change Modeler Module of Terrset. Flood risk was computed by combining hazard, exposure, and vulnerability using hydrodynamic modeling and the Analytic Hierarchy Process method. We have compared flood risk in 1995, 2019, and 2040. Although flood risk increases with urbanization, population density, and the number of hospitals in the flood plain, especially in the coastal region, the area exposed to high and very high risks decreases due to a reduction in poverty rate. This study can provide a theoretical framework supporting climate change related to risk assessment in other metropolitan regions. Methodologically, it underlines the importance of using satellite imagery and the continuity of data in the planning-related decision-making process.

ACS Style

Huu Duy Nguyen; Dennis Fox; Dinh Kha Dang; Le Tuan Pham; Quan Vu Viet Du; Thi Ha Thanh Nguyen; Thi Ngoc Dang; Van Truong Tran; Phuong Lan Vu; Quoc-Huy Nguyen; Tien Giang Nguyen; Quang-Thanh Bui; Alexandru-Ionut Petrisor. Predicting Future Urban Flood Risk Using Land Change and Hydraulic Modeling in a River Watershed in the Central Province of Vietnam. Remote Sensing 2021, 13, 262 .

AMA Style

Huu Duy Nguyen, Dennis Fox, Dinh Kha Dang, Le Tuan Pham, Quan Vu Viet Du, Thi Ha Thanh Nguyen, Thi Ngoc Dang, Van Truong Tran, Phuong Lan Vu, Quoc-Huy Nguyen, Tien Giang Nguyen, Quang-Thanh Bui, Alexandru-Ionut Petrisor. Predicting Future Urban Flood Risk Using Land Change and Hydraulic Modeling in a River Watershed in the Central Province of Vietnam. Remote Sensing. 2021; 13 (2):262.

Chicago/Turabian Style

Huu Duy Nguyen; Dennis Fox; Dinh Kha Dang; Le Tuan Pham; Quan Vu Viet Du; Thi Ha Thanh Nguyen; Thi Ngoc Dang; Van Truong Tran; Phuong Lan Vu; Quoc-Huy Nguyen; Tien Giang Nguyen; Quang-Thanh Bui; Alexandru-Ionut Petrisor. 2021. "Predicting Future Urban Flood Risk Using Land Change and Hydraulic Modeling in a River Watershed in the Central Province of Vietnam." Remote Sensing 13, no. 2: 262.

Journal article
Published: 30 November 2020 in Journal of Hydrology
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Flood risk assessment is an important task for disaster management activities in flood-prone areas. Therefore, it is crucial to develop accurate flood risk assessment maps. In this study, we proposed a flood risk assessment framework which combines flood susceptibility assessment and flood consequences (human health and financial impact) for developing a final flood risk assessment map using Multi-Criteria Decision Analysis (MCDA) method. Two hybrid Artificial Intelligence (AI) models, namely ABMDT (AdaBoost-DT) and BDT (Bagging-DT) were developed with Decision Table (DT) as a base classifier for creating a flood susceptibility map. We used 847 flood locations of major flooding events in the years 2007, 2009 and 2013 in Quang Nam province of Vietnam; and 14 flood influencing factors of topography, geology, hydrology and environment to construct and validate the hybrid AI models. Various statistical measures were used to validate the models, including the Area Under Receiver Operating Characteristic (ROC) Curve called AUC. Results show that all the proposed models performed well, but the performance of the BDT model (AUC=0.96) is the best in comparison to other models ABMDT (AUC=0.953) and single DT (AUC=0.929). Therefore, the flood susceptibility map produced by the BDT model was used to combine with a flood consequences map to develop a reliable flood risk assessment map for the study area. The final flood risk map can provide a useful source for better flood hazard management of the study area, and the proposed framework and models can be applied to other flood-prone areas.

ACS Style

Binh Thai Pham; Chinh Luu; Tran Van Phong; Huu Duy Nguyen; Hiep Van Le; Thai Quoc Tran; Huong Thu Ta; Indra Prakash. Flood risk assessment using hybrid artificial intelligence models integrated with multi-criteria decision analysis in Quang Nam Province, Vietnam. Journal of Hydrology 2020, 592, 125815 .

AMA Style

Binh Thai Pham, Chinh Luu, Tran Van Phong, Huu Duy Nguyen, Hiep Van Le, Thai Quoc Tran, Huong Thu Ta, Indra Prakash. Flood risk assessment using hybrid artificial intelligence models integrated with multi-criteria decision analysis in Quang Nam Province, Vietnam. Journal of Hydrology. 2020; 592 ():125815.

Chicago/Turabian Style

Binh Thai Pham; Chinh Luu; Tran Van Phong; Huu Duy Nguyen; Hiep Van Le; Thai Quoc Tran; Huong Thu Ta; Indra Prakash. 2020. "Flood risk assessment using hybrid artificial intelligence models integrated with multi-criteria decision analysis in Quang Nam Province, Vietnam." Journal of Hydrology 592, no. : 125815.

Journal article
Published: 17 June 2020 in Symmetry
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Predicting and mapping fire susceptibility is a top research priority in fire-prone forests worldwide. This study evaluates the abilities of the Bayes Network (BN), Naïve Bayes (NB), Decision Tree (DT), and Multivariate Logistic Regression (MLP) machine learning methods for the prediction and mapping fire susceptibility across the Pu Mat National Park, Nghe An Province, Vietnam. The modeling methodology was formulated based on processing the information from the 57 historical fires and a set of nine spatially explicit explanatory variables, namely elevation, slope degree, aspect, average annual temperate, drought index, river density, land cover, and distance from roads and residential areas. Using the area under the receiver operating characteristic curve (AUC) and seven other performance metrics, the models were validated in terms of their abilities to elucidate the general fire behaviors in the Pu Mat National Park and to predict future fires. Despite a few differences between the AUC values, the BN model with an AUC value of 0.96 was dominant over the other models in predicting future fires. The second best was the DT model (AUC = 0.94), followed by the NB (AUC = 0.939), and MLR (AUC = 0.937) models. Our robust analysis demonstrated that these models are sufficiently robust in response to the training and validation datasets change. Further, the results revealed that moderate to high levels of fire susceptibilities are associated with ~19% of the Pu Mat National Park where human activities are numerous. This study and the resultant susceptibility maps provide a basis for developing more efficient fire-fighting strategies and reorganizing policies in favor of sustainable management of forest resources.

ACS Style

Binh Thai Pham; Abolfazl Jaafari; Mohammadtaghi Avand; Nadhir Al-Ansari; Tran Dinh Du; Hoang Phan Hai Yen; Tran Van Phong; Duy Huu Nguyen; Hiep Van Le; Davood Mafi-Gholami; Indra Prakash; Hoang Thi Thuy; Tran Thi Tuyen. Performance Evaluation of Machine Learning Methods for Forest Fire Modeling and Prediction. Symmetry 2020, 12, 1022 .

AMA Style

Binh Thai Pham, Abolfazl Jaafari, Mohammadtaghi Avand, Nadhir Al-Ansari, Tran Dinh Du, Hoang Phan Hai Yen, Tran Van Phong, Duy Huu Nguyen, Hiep Van Le, Davood Mafi-Gholami, Indra Prakash, Hoang Thi Thuy, Tran Thi Tuyen. Performance Evaluation of Machine Learning Methods for Forest Fire Modeling and Prediction. Symmetry. 2020; 12 (6):1022.

Chicago/Turabian Style

Binh Thai Pham; Abolfazl Jaafari; Mohammadtaghi Avand; Nadhir Al-Ansari; Tran Dinh Du; Hoang Phan Hai Yen; Tran Van Phong; Duy Huu Nguyen; Hiep Van Le; Davood Mafi-Gholami; Indra Prakash; Hoang Thi Thuy; Tran Thi Tuyen. 2020. "Performance Evaluation of Machine Learning Methods for Forest Fire Modeling and Prediction." Symmetry 12, no. 6: 1022.

Journal article
Published: 10 April 2020 in Sustainability
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Vietnam has been extensively affected by floods, suffering heavy losses in human life and property. While the Vietnamese government has focused on structural measures of flood defence such as levees and early warning systems, the country still lacks flood risk assessment methodologies and frameworks at local and national levels. In response to this gap, this study developed a flood risk assessment framework that uses historical flood mark data and a high-resolution digital elevation model to create an inundation map, then combined this map with exposure and vulnerability data to develop a holistic flood risk assessment map. The case study is the October 2010 flood event in Quang Binh province, which caused 74 deaths, 210 injuries, 188,628 flooded properties, 9019 ha of submerged and damaged agricultural land, and widespread damages to canals, levees, and roads. The final flood risk map showed a total inundation area of 64,348 ha, in which 8.3% area of low risk, 16.3% area of medium risk, 12.0% area of high risk, 37.1% area of very high risk, and 26.2% area of extremely high risk. The holistic flood risk assessment map of Quang Binh province is a valuable tool and source for flood preparedness activities at the local scale.

ACS Style

Chinh Luu; Hieu Xuan Tran; Binh Thai Pham; Nadhir Al-Ansari; Thai Quoc Tran; Nga Quynh Duong; Nam Hai Dao; Lam Phuong Nguyen; Huu Duy Nguyen; Huong Thu Ta; Hiep Van Le; Jason Von Meding. Framework of Spatial Flood Risk Assessment for a Case Study in Quang Binh Province, Vietnam. Sustainability 2020, 12, 3058 .

AMA Style

Chinh Luu, Hieu Xuan Tran, Binh Thai Pham, Nadhir Al-Ansari, Thai Quoc Tran, Nga Quynh Duong, Nam Hai Dao, Lam Phuong Nguyen, Huu Duy Nguyen, Huong Thu Ta, Hiep Van Le, Jason Von Meding. Framework of Spatial Flood Risk Assessment for a Case Study in Quang Binh Province, Vietnam. Sustainability. 2020; 12 (7):3058.

Chicago/Turabian Style

Chinh Luu; Hieu Xuan Tran; Binh Thai Pham; Nadhir Al-Ansari; Thai Quoc Tran; Nga Quynh Duong; Nam Hai Dao; Lam Phuong Nguyen; Huu Duy Nguyen; Huong Thu Ta; Hiep Van Le; Jason Von Meding. 2020. "Framework of Spatial Flood Risk Assessment for a Case Study in Quang Binh Province, Vietnam." Sustainability 12, no. 7: 3058.

Journal article
Published: 12 December 2019 in Sustainability
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Landslides affect properties and the lives of a large number of people in many hilly parts of Vietnam and in the world. Damages caused by landslides can be reduced by understanding distribution, nature, mechanisms and causes of landslides with the help of model studies for better planning and risk management of the area. Development of landslide susceptibility maps is one of the main steps in landslide management. In this study, the main objective is to develop GIS based hybrid computational intelligence models to generate landslide susceptibility maps of the Da Lat province, which is one of the landslide prone regions of Vietnam. Novel hybrid models of alternating decision trees (ADT) with various ensemble methods, namely bagging, dagging, MultiBoostAB, and RealAdaBoost, were developed namely B-ADT, D-ADT, MBAB-ADT, RAB-ADT, respectively. Data of 72 past landslide events was used in conjunction with 11 landslide conditioning factors (curvature, distance from geological boundaries, elevation, land use, Normalized Difference Vegetation Index (NDVI), relief amplitude, stream density, slope, lithology, weathering crust and soil) in the development and validation of the models. Area under the receiver operating characteristic (ROC) curve (AUC), and several statistical measures were applied to validate these models. Results indicated that performance of all the models was good (AUC value greater than 0.8) but B-ADT model performed the best (AUC= 0.856). Landslide susceptibility maps generated using the proposed models would be helpful to decision makers in the risk management for land use planning and infrastructure development.

ACS Style

Viet-Tien Nguyen; Trong Hien Tran; Ngoc Anh Ha; Van Liem Ngo; Al-Ansari Nadhir; Van Phong Tran; Huu Duy Nguyen; Malek M. A.; Ata Amini; Indra Prakash; L.S. Ho; Binh Thai Pham. GIS Based Novel Hybrid Computational Intelligence Models for Mapping Landslide Susceptibility: A Case Study at Da Lat City, Vietnam. Sustainability 2019, 11, 7118 .

AMA Style

Viet-Tien Nguyen, Trong Hien Tran, Ngoc Anh Ha, Van Liem Ngo, Al-Ansari Nadhir, Van Phong Tran, Huu Duy Nguyen, Malek M. A., Ata Amini, Indra Prakash, L.S. Ho, Binh Thai Pham. GIS Based Novel Hybrid Computational Intelligence Models for Mapping Landslide Susceptibility: A Case Study at Da Lat City, Vietnam. Sustainability. 2019; 11 (24):7118.

Chicago/Turabian Style

Viet-Tien Nguyen; Trong Hien Tran; Ngoc Anh Ha; Van Liem Ngo; Al-Ansari Nadhir; Van Phong Tran; Huu Duy Nguyen; Malek M. A.; Ata Amini; Indra Prakash; L.S. Ho; Binh Thai Pham. 2019. "GIS Based Novel Hybrid Computational Intelligence Models for Mapping Landslide Susceptibility: A Case Study at Da Lat City, Vietnam." Sustainability 11, no. 24: 7118.